Rethinking Engineering Productivity Metrics in the AI Era

AI-assisted development weakens the link between engineering output and engineering value because faster code generation does not necessarily improve delivery outcomes. The article proposes measuring productivity across five dimensions: delivery outcomes, engineering quality, flow efficiency, business impact, and team capability. Traditional indicators such as velocity, commits, and DORA metrics remain useful but provide only part of the picture. Technology leaders should assess whether AI improves reliability, decision-making, customer value, and long-term engineering capability rather than simply increasing development activity.